Introduction

Artificial Intelligence (AI) and Machine Learning (ML) can turn your data into insightful knowledge and financial gain in today's data-driven world. It is uncommon to obtain the perfect combination of precise, well-labelled, and copious data. When starting an AI project, how can we overcome these data-related challenges? This blog examines real-world issues and fixes to make sure your AI projects get off to a strong start.

Data Quality: AI's Foundation

For any AI effort to be successful, data quality is essential. Your project may be in danger if you encounter common problems including data shortages, incomplete datasets, data inaccuracies, inadequate data on certain examples, and faulty labelling. Here, we offer workable answers to these problems.

1. Scarcity or Absence of Data

Lack of data is a major obstacle to building efficient ML models. Models with little data may not perform well or may not be able to generalize. Large Language Models (LLMs) such as ChatGPT and Llama provide an answer by either augmenting the model with real, contextually relevant data samples from available sources or creating synthetic data that imitates real-world circumstances. This method expedites the creation of machine learning applications in a variety of domains and aids in overcoming data scarcity.

Case Study: HR Department of a Large German Marketing Agency Objective: Resume processing using machine learning.

Solution: Since the customer lacked a database to train the model on, we generated job requests and examined incoming openings using the LLM model. Given that the customer handles personal information, LLM

2. Missing Information

A lack of data can impair machine learning models' performance. There are two approaches to deal with this:

• Iterative Development: To improve the ML system, start a new development phase.
• Entire Initial Data Provision: At the beginning of the project, supply the entire set of anticipated data.

Vehicle Route Prediction System, for instance. Vehicle routes in a specific area were predicted by a system driven by ValueXI. Performance problems arose when the client sought to use the system in a different region after it had been deployed. If thorough initial data had been provided, this issue might have been avoided.

3. Errors in Data

The integrity of machine learning models can be jeopardized by data inaccuracies, especially in systems where information is manually entered. Thorough data purification is necessary to find and fix mistakes before they affect how the training is conducted. Stricter verification procedures guarantee correct and dependable data used in machine learning, which leads to more dependable model results.

4. Limited Information on Particular Cases

If nothing is known about the samples, a high data volume does not ensure a functional machine learning model. Typical remedies consist of:

• Data Augmentation: Increase model training by obtaining additional data.
• Task Segmentation: Divide the main task into more manageable, smaller components.
• Goal Reframing: If required, change the project's focus to a more attainable goal.

For instance, the internal system of a travel agency The rapid increase of the company's clientele exceeded its internal system. First, we wanted to find out what kinds of requests customers made and where they were most likely to buy. But the information at hand was inadequate. Our attention was diverted to determining the probability that clients would return calls quickly, which led to more efficient lead nurturing and higher revenue.

5. Mislabelled Labels

To train machine learning models, precise labelling is essential. Including internal specialists in the labelling procedure can greatly improve the model's performance and lower the possibility of mistakes.

Case Study: Provider of Medical Equipment We had to annotate ultrasound pictures for a carotid artery screening solution as part of a project for a provider of medical equipment. This necessitated domain knowledge and resulted in higher time and expense expenditures that could have been avoided with internal knowledge.

6. Data Privacy Issues

Companies may be reluctant to give their data to outside parties because they view it as a valuable asset. Sensitive information can be kept private when creating a proof of concept using anonymized data.

Case Study: International Producer of Electronics and Home Appliances Business Objective: Create a platform that analyses data from customer and technical support staff conversations. Resolution: Using ChatGPT, we organized the extraction of entities from source dialogues, anonymized every dialog to omit personal information, and modified prompt extraction for the LLM at hand. This dialog analysis tool assisted in identifying common queries, rating customer happiness, and analysing the work of technical support employees.

ValueXI: The Answer to Your Data-Related Problems

ValueXI provides a thorough answer to a range of data-related problems:

• Assures careful data preparation and quality and completeness checks.
• Identifies anomalous data patterns automatically.
• Promotes data privacy by making anonymization possible.
• Provides transparency and control over how results are interpreted.
• Provides opportunity for businesses managing sensitive data or lacking in data.
• Constantly evaluates model performance using fresh data.

You may verify whether your dataset is appropriate for AI training with ValueXI's Dataset Validation tool. The Dataset Analytics report offers thorough explanations and recommendations for enhancements. ValueXI walks you through a tried-and-true project creation workflow with just a click of the "Start Training" button, with assistance from our Data Science team.